Intelligent video surveillance systems are rapidly being introduced to public places. The adoption of computer vision and machine learning techniques enables various applications for collected video features; one of the major is safety monitoring. The efficacy of violent event detection is measured by the efficiency and accuracy of violent event detection. In this paper, we present a novel architecture for violence detection from video surveillance cameras. Our proposed model is a spatial feature extracting a U-Net-like network that uses MobileNet V2 as an encoder followed by LSTM for temporal feature extraction and classification. The proposed model is computationally light and still achieves good results—experiments showed that an average accuracy is 0.82 ± 2% and average precision is 0.81 ± 3% using a complex real-world security camera footage dataset based on RWF-2000.
Abstract-The object of this paper is to develop an emotion recognition system that analysis the motion trajectory of the eye and gives the response on appraisal emotion. The emotion recognition solution is based on the data gathering using head mounted eye tracking device. The participants of experimental investigation were provided with a visual stimulus (PowerPoint slides) and the emotional feedback was determined by the combination of eye tracking device and emotion recognition software. The stimulus was divided in four groups by the emotion that should be triggered in the human, i.e., neutral, disgust, exhilaration and excited. Some initial experiments and the data on the recognition accuracy of the emotion from eye motion trajectory are provided along with the description of implemented algorithms.
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